Multispectral Imaging Sensor for Surgical Tissue Analysis
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Solution Overview
Problem
Existing medical imaging technologies lack the capability to provide accurate, real-time multispectral, hyperspectral, and mosaic imaging for surgical procedures, limiting the precision and flexibility of surgical operations and autonomous robotic systems.
Innovation Solution
A system utilizing a single sensor with multiple pixels sensitive to different wavelengths and bands, capable of generating multispectral, hyperspectral, and mosaic images, combined with a processing unit for quantitative analysis of tissue features, enabling real-time image overlays and enhanced surgical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensors are used to capture different imaging modalities, then imaging capability and information completeness are improved, but device complexity and system size increase
Solution Approach 1:
The patent merges multiple imaging modalities (RGB, fluorescence, laser speckle, depth, hyperspectral) into a single sensor device. The sensor integrates multiple pixel types that can simultaneously capture different types of optical signals, eliminating the need for separate sensors for each imaging modality and thereby reducing system complexity while maintaining comprehensive imaging capability.
Solution Approach 2:
The sensor is designed with universal functionality to perform multiple imaging tasks simultaneously. Each pixel or pixel group can detect different wavelengths and signal types, allowing a single device to serve as an RGB camera, fluorescence camera, laser speckle camera, depth camera, and hyperspectral camera all at once.
2Measurement precision
If multiple sensors are used for different imaging modalities, then imaging accuracy and information completeness are improved, but the number of components and calibration requirements increase
Solution Approach 1:
The patent combines multiple sensing functions into a single integrated sensor, reducing the number of components that need to be calibrated and synchronized. By merging RGB, fluorescence, laser speckle, depth, and hyperspectral detection capabilities into one device, the system eliminates the complexity of multi-sensor calibration while maintaining high imaging accuracy through unified optical path design.
3Device complexity
If a single sensor with multiple pixels is used, then device complexity is reduced, but the ability to perform quantitative analysis of specific features may be compromised
Solution Approach 1:
The sensor is segmented into multiple pixel types or pixel groups, each optimized for detecting specific wavelength ranges or signal characteristics. This segmentation allows the single sensor to maintain the quantitative analysis capability of multiple specialized sensors while reducing overall system complexity. Each pixel segment can be independently calibrated and processed to provide precise measurements of fluorescence, absorbance, and other optical properties.
Solution Approach 2:
Different regions or types of pixels within the sensor are assigned different spectral sensitivities and detection characteristics tailored to their specific functions. This local quality differentiation enables the sensor to perform quantitative analysis of specific features (such as tissue oxygenation or blood flow) while maintaining a unified, simpler device architecture.
4Productivity
If real-time imaging is implemented, then surgical decision-making speed is improved, but data processing requirements and computational complexity increase
Solution Approach 1:
The sensor and processing system perform preliminary actions by pre-calibrating pixel responses and pre-processing algorithms to handle the complexity of multi-modality data fusion. By preparing the processing pipeline in advance and using optimized algorithms that leverage the specific characteristics of each pixel type, the system achieves real-time processing without requiring excessive computational resources during actual surgical procedures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances surgical precision and flexibility by providing real-time, multispectral imaging data for improved tissue analysis and robotic control, allowing for accurate quantification of fluorescence and blood oxygenation, and aiding in surgical procedures.
Implementation Method 1
one or more imaging sensors configured to capture an image of a tissue... capable of multi-wavelength imaging
Implementation Method 2
quantitative analysis of one or more features or fiducials that are detectable within the image of the tissue based on one or more light signals obtained or registered using each of the first plurality of sub-pixels and the second plurality of sub-pixels
Implementation Method 3
The processing unit is configured to perform a quantitative analysis of one or more features or fiducials that are detectable within the image of the tissue based on one or more light signals obtained or registered using each of the first plurality of sub-pixels and the second plurality of sub-pixels
Data Source
AI summary
The present disclosure provides a system for medical imaging. The system may comprise one or more imaging sensors for imaging a surgical scene. Each of the one or more imaging sensors may comprise a plurality of pixels. At least one pixel of the plurality of pixels may comprise a plurality of sub-pixels sensitive to different bands or wavelengths of light. The system may further comprise a processing unit operatively coupled to the one or more imaging sensors. The processing unit may be configured to perform a quantitative analysis of one or more features or fiducials that are detectable within the surgical scene based on one or more light signals obtained or registered using the plurality of sub-pixels.